2025
VeriCoder: Enhancing LLM-Based RTL Code Generation through Functional Correctness Validation
Anjiang Wei, Huanmi Tan, Tarun Suresh, Daniel Mendoza, Thiago SFX Teixeira, Ke Wang, Caroline Trippel, Alex Aiken
arXiv preprint Under review. 2025
VeriCoder is a model for RTL (Register Transfer Level) code generation, fine-tuned on a novel dataset that is functionally validated via feedback-directed refinement. Unlike prior datasets that only ensure syntactic correctness, our dataset guarantees that each RTL design passes automatically generated unit tests aligned with its natural language specification. Our key contributions include: (1) a large-scale dataset of 125,000+ examples with simulation-passing RTL designs, (2) a feedback-driven construction methodology that iteratively refines designs and tests based on test results, (3) superior performance with up to +71.7% relative improvement on VerilogEval benchmarks, and (4) comprehensive resources including dataset, model weights, inference scripts, and training pipeline.
VeriCoder: Enhancing LLM-Based RTL Code Generation through Functional Correctness Validation
Anjiang Wei, Huanmi Tan, Tarun Suresh, Daniel Mendoza, Thiago SFX Teixeira, Ke Wang, Caroline Trippel, Alex Aiken
arXiv preprint Under review. 2025
VeriCoder is a model for RTL (Register Transfer Level) code generation, fine-tuned on a novel dataset that is functionally validated via feedback-directed refinement. Unlike prior datasets that only ensure syntactic correctness, our dataset guarantees that each RTL design passes automatically generated unit tests aligned with its natural language specification. Our key contributions include: (1) a large-scale dataset of 125,000+ examples with simulation-passing RTL designs, (2) a feedback-driven construction methodology that iteratively refines designs and tests based on test results, (3) superior performance with up to +71.7% relative improvement on VerilogEval benchmarks, and (4) comprehensive resources including dataset, model weights, inference scripts, and training pipeline.
2024
Convallis a cras semper auctor neque vitae rutrum quisque non tellus orci ac
Your Name, James Wang, Some Other Name, John Doe
International Conference on Machine Learning (ICML) 2024 Spotlight
Photo by Pineapple Supply Co. on Unsplash. Please put a tldr (too-long-didnt-read, 1~2 sentences) of your publication here. It is not recommended to put the actual abstract here because it is usually too long to fit in. $\LaTeX$ is supported. $a=b+c$.
Convallis a cras semper auctor neque vitae rutrum quisque non tellus orci ac
Your Name, James Wang, Some Other Name, John Doe
International Conference on Machine Learning (ICML) 2024 Spotlight
Photo by Pineapple Supply Co. on Unsplash. Please put a tldr (too-long-didnt-read, 1~2 sentences) of your publication here. It is not recommended to put the actual abstract here because it is usually too long to fit in. $\LaTeX$ is supported. $a=b+c$.